WRANGLERS
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Obviously, we all look at things through the filter of our own experiences….
— Malcolm-Jamal Warner
df <- read.csv('./archetypes/access-to-water-and-sanitation/access-to-water-and-sanitation.csv', header = TRUE, stringsAsFactors = FALSE)
df
Filter where rows equal to Africa in the Continent column.
df_1 <- df %>% filter(Continent=="Africa")
df_1
Taking only African countries into consideration, let’s say we want to eliminate, for some reason, Angola from Country from this data
df_2 <- df %>% filter((Continent=="Africa") & !(Country=="Angola"))
df_2
Filter dataset where Population is greater than 50,000.
df_6 <- df_2%>% filter(Population > 50000)
df_6
Filter dataset where Population is less than 50,000.
df_8 <- df_2 %>% filter(Population < 50000)
df_8
First, let’s convert the column Date into date format.
df_9 <- df
df_9$Date<-as.Date(df_9$Date, tryFormats = c("%Y-%m-%d"),optional = FALSE)
df_9
Filter dataset where Date is between 2010-2015:
df_10 <- df_9%>% filter(between(Date, as.Date("2010-01-01"), as.Date("2015-01-01")))
df_10
Filter rows where Date is before 2000.
df_11 <- df_9 %>% filter(Date < as.Date("2000-01-01"))
df_11
Filter rows where Date is after 2010 for the countries with No access to water for a population that is bigger than 75,000.
df_12 <- df_9%>% filter(Date > as.Date("2010-01-01") & (Metric=="No access to water") & (Population > 75000))
df_12
Filter the dataset where Continent name starts with S:
df_13 <- df %>% filter(str_detect(Continent, "^S"))
df_13
@misc{unicef_sdg,
author = {UNICEF},
title = {SDG Goal 6: Clean Water and Sanitation},
url = {https://data.unicef.org/sdgs/goal-6-clean-water-sanitation/#:~:text=Goal%206%20aims%20to%20ensure%20availability%20and%20sustainable},
urldate = {2021-03-18},
organization = {UNICEF DATA}
}